{"results":[{"id":"ss_7c8aa9420e1f1e8c282a26d4e91da3f66722c3ab","title":"The International Federation of Gynecology and Obstetrics (FIGO) initiative on pre‐eclampsia: A pragmatic guide for first‐trimester screening and prevention","authors":[{"name":"L. Poon"},{"name":"A. Shennan"},{"name":"J. Hyett"},{"name":"A. Kapur"},{"name":"E. Hadar"},{"name":"H. Divakar"},{"name":"F. Mcauliffe"},{"name":"F. S. Silva Costa"},{"name":"P. Dadelszen"},{"name":"H. Mcintyre"},{"name":"A. Kihara"},{"name":"G. D. Di Renzo"},{"name":"R. Romero"},{"name":"M. D’Alton"},{"name":"V. Berghella"},{"name":"K. Nicolaides"},{"name":"M. Hod"}],"abstract":"Pre‐eclampsia (PE) is a multisystem disorder that typically affects 2%–5% of pregnant women and is one of the leading causes of maternal and perinatal morbidity and mortality, especially when the condition is of early onset. Globally, 76 000 women and 500 000 babies die each year from this disorder. Furthermore, women in low‐resource countries are at a higher risk of developing PE compared with those in high‐resource countries. Although a complete understanding of the pathogenesis of PE remains unclear, the current theory suggests a two‐stage process. The first stage is caused by shallow invasion of the trophoblast, resulting in inadequate remodeling of the spiral arteries. This is presumed to lead to the second stage, which involves the maternal response to endothelial dysfunction and imbalance between angiogenic and antiangiogenic factors, resulting in the clinical features of the disorder. Accurate prediction and uniform prevention continue to elude us. The quest to effectively predict PE in the first trimester of pregnancy is fueled by the desire to identify women who are at high risk of developing PE, so that necessary measures can be initiated early enough to improve placentation and thus prevent or at least reduce the frequency of its occurrence. Furthermore, identification of an “at risk” group will allow tailored prenatal surveillance to anticipate and recognize the onset of the clinical syndrome and manage it promptly. PE has been previously defined as the onset of hypertension accompanied by significant proteinuria after 20 weeks of gestation. Recently, the definition of PE has been broadened. Now the internationally agreed definition of PE is the one proposed by the International Society for the Study of Hypertension in Pregnancy (ISSHP). According to the ISSHP, PE is defined as systolic blood pressure at ≥140 mm Hg and/or diastolic blood pressure at ≥90 mm Hg on at least two occasions measured 4 hours apart in previously normotensive women and is accompanied by one or more of the following new‐onset conditions at or after 20 weeks of gestation: 1.Proteinuria (i.e. ≥30 mg/mol protein:creatinine ratio; ≥300 mg/24 hour; or ≥2 + dipstick); 2.Evidence of other maternal organ dysfunction, including: acute kidney injury (creatinine ≥90 μmol/L; 1 mg/dL); liver involvement (elevated transaminases, e.g. alanine aminotransferase or aspartate aminotransferase \u003e40 IU/L) with or without right upper quadrant or epigastric abdominal pain; neurological complications (e.g. eclampsia, altered mental status, blindness, stroke, clonus, severe headaches, and persistent visual scotomata); or hematological complications (thrombocytopenia–platelet count \u003c150 000/μL, disseminated intravascular coagulation, hemolysis); or 3.Uteroplacental dysfunction (such as fetal growth restriction, abnormal umbilical artery Doppler waveform analysis, or stillbirth). It is well established that a number of maternal risk factors are associated with the development of PE: advanced maternal age; nulliparity; previous history of PE; short and long interpregnancy interval; use of assisted reproductive technologies; family history of PE; obesity; Afro‐Caribbean and South Asian racial origin; co‐morbid medical conditions including hyperglycemia in pregnancy; pre‐existing chronic hypertension; renal disease; and autoimmune diseases, such as systemic lupus erythematosus and antiphospholipid syndrome. These risk factors have been described by various professional organizations for the identification of women at risk of PE; however, this approach to screening is inadequate for effective prediction of PE. PE can be subclassified into: 1.Early‐onset PE (with delivery at \u003c34+0 weeks of gestation); 2.Preterm PE (with delivery at \u003c37+0 weeks of gestation); 3.Late‐onset PE (with delivery at ≥34+0 weeks of gestation); 4.Term PE (with delivery at ≥37+0 weeks of gestation). These subclassifications are not mutually exclusive. Early‐onset PE is associated with a much higher risk of short‐ and long‐term maternal and perinatal morbidity and mortality. Obstetricians managing women with preterm PE are faced with the challenge of balancing the need to achieve fetal maturation in utero with the risks to the mother and fetus of continuing the pregnancy longer. These risks include progression to eclampsia, development of placental abruption and HELLP (hemolysis, elevated liver enzyme, low platelet) syndrome. On the other hand, preterm delivery is associated with higher infant mortality rates and increased morbidity resulting from small for gestational age (SGA), thrombocytopenia, bronchopulmonary dysplasia, cerebral palsy, and an increased risk of various chronic diseases in adult life, particularly type 2 diabetes, cardiovascular disease, and obesity. Women who have experienced PE may also face additional health problems in later life, as the condition is associated with an increased risk of death from future cardiovascular disease, hypertension, stroke, renal impairment, metabolic syndrome, and diabetes. The life expectancy of women who developed preterm PE is reduced on average by 10 years. There is also significant impact on the infants in the long term, such as increased risks of insulin resistance, diabetes mellitus, coronary artery disease, and hypertension in infants born to pre‐eclamptic women. The International Federation of Gynecology and Obstetrics (FIGO) brought together international experts to discuss and evaluate current knowledge on PE and develop a document to frame the issues and suggest key actions to address the health burden posed by PE. FIGO's objectives, as outlined in this document, are: (1) To raise awareness of the links between PE and poor maternal and perinatal outcomes, as well as to the future health risks to mother and offspring, and demand a clearly defined global health agenda to tackle this issue; and (2) To create a consensus document that provides guidance for the first‐trimester screening and prevention of preterm PE, and to disseminate and encourage its use. Based on high‐quality evidence, the document outlines current global standards for the first‐trimester screening and prevention of preterm PE, which is in line with FIGO good clinical practice advice on first trimester screening and prevention of pre‐eclampsia in singleton pregnancy.1 It provides both the best and the most pragmatic recommendations according to the level of acceptability, feasibility, and ease of implementation that have the potential to produce the most significant impact in different resource settings. Suggestions are provided for a variety of different regional and resource settings based on their financial, human, and infrastructure resources, as well as for research priorities to bridge the current knowledge and evidence gap. To deal with the issue of PE, FIGO recommends the following: Public health focus: There should be greater international attention given to PE and to the links between maternal health and noncommunicable diseases (NCDs) on the Sustainable Developmental Goals agenda. Public health measures to increase awareness, access, affordability, and acceptance of preconception counselling, and prenatal and postnatal services for women of reproductive age should be prioritized. Greater efforts are required to raise awareness of the benefits of early prenatal visits targeted at reproductive‐aged women, particularly in low‐resource countries. Universal screening: All pregnant women should be screened for preterm PE during early pregnancy by the first‐trimester combined test with maternal risk factors and biomarkers as a one‐step procedure. The risk calculator is available free of charge at https://fetalmedicine.org/research/assess/preeclampsia. FIGO encourages all countries and its member associations to adopt and promote strategies to ensure this. The best combined test is one that includes maternal risk factors, measurements of mean arterial pressure (MAP), serum placental growth factor (PLGF), and uterine artery pulsatility index (UTPI). Where it is not possible to measure PLGF and/or UTPI, the baseline screening test should be a combination of maternal risk factors with MAP, and not maternal risk factors alone. If maternal serum pregnancy‐associated plasma protein A (PAPP‐A) is measured for routine first‐trimester screening for fetal aneuploidies, the result can be included for PE risk assessment. Variations to the full combined test would lead to a reduction in the performance screening. A woman is considered high risk when the risk is 1 in 100 or more based on the first‐trimester combined test with maternal risk factors, MAP, PLGF, and UTPI. Contingent screening: Where resources are limited, routine screening for preterm PE by maternal factors and MAP in all pregnancies and reserving measurements of PLGF and UTPI for a subgroup of the population (selected on the basis of the risk derived from screening by maternal factors and MAP) can be considered. Prophylactic measures: Following first‐trimester screening for preterm PE, women identified at high risk should receive aspirin prophylaxis commencing at 11–14+6 weeks of gestation at a dose of ~150 mg to be taken every night until 36 weeks of gestation, when delivery occurs, or when PE is diagnosed. Low‐dose aspirin should not be prescribed to all pregnant women. In women with low calcium intake (\u003c800 mg/d), either calcium replacement (≤1 g elemental calcium/d) or calcium supplementation (1.5–2 g elemental calcium/d) may reduce the burden of both early‐ and late‐onset PE.","source":"Semantic Scholar","year":2019,"language":"en","subjects":["Medicine"],"doi":"10.1002/ijgo.12802","url":"https://www.semanticscholar.org/paper/7c8aa9420e1f1e8c282a26d4e91da3f66722c3ab","pdf_url":"https://obgyn.onlinelibrary.wiley.com/doi/pdfdirect/10.1002/ijgo.12802","is_open_access":true,"citations":1035,"published_at":"","score":93},{"id":"ss_5232426f030ac315a044f89858d0cf437f98f682","title":"FIGO (International Federation of Gynecology and Obstetrics) initiative on fetal growth: Best practice advice for screening, diagnosis, and management of fetal growth restriction","authors":[{"name":"N. Melamed"},{"name":"A. Baschat"},{"name":"Y. Yinon"},{"name":"A. Athanasiadis"},{"name":"F. Mecacci"},{"name":"F. Figueras"},{"name":"V. Berghella"},{"name":"A. Nazareth"},{"name":"M. Tahlak"},{"name":"H. David McIntyre"},{"name":"F. da Silva Costa"},{"name":"Anne B. Kihara"},{"name":"E. Hadar"},{"name":"F. Mcauliffe"},{"name":"M. Hanson"},{"name":"Ronald C. Ma"},{"name":"Rachel Gooden"},{"name":"E. Sheiner"},{"name":"A. Kapur"},{"name":"H. Divakar"},{"name":"D. Ayres-de-Campos"},{"name":"L. Hiersch"},{"name":"L. Poon"},{"name":"J. Kingdom"},{"name":"R. Romero"},{"name":"M. Hod"}],"abstract":"Nir Melamed1 | Ahmet Baschat2 | Yoav Yinon3 | Apostolos Athanasiadis4 | Federico Mecacci5 | Francesc Figueras6 | Vincenzo Berghella7 | Amala Nazareth8 | Muna Tahlak9 | H. David McIntyre10 | Fabrício Da Silva Costa11 | Anne B. Kihara12 | Eran Hadar13,14 | Fionnuala McAuliffe15 | Mark Hanson16,17 | Ronald C. Ma18,19 | Rachel Gooden20 | Eyal Sheiner21 | Anil Kapur22 | Hema Divakar23 | Diogo Ayres-de-Campos24 | Liran Hiersch25 | Liona C. Poon26 | John Kingdom27 | Roberto Romero28 | Moshe Hod13,14*","source":"Semantic Scholar","year":2021,"language":"en","subjects":["Medicine"],"doi":"10.1002/ijgo.13522","url":"https://www.semanticscholar.org/paper/5232426f030ac315a044f89858d0cf437f98f682","pdf_url":"https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/ijgo.13522","is_open_access":true,"citations":395,"published_at":"","score":76.85},{"id":"ss_364012e7f5d8e0e37d84f2b87bc3c116528a4993","title":"The Exciting Potential for ChatGPT in Obstetrics and Gynecology.","authors":[{"name":"A. Grünebaum"},{"name":"J. Chervenak"},{"name":"Susan L. Pollet"},{"name":"A. Katz"},{"name":"F. Chervenak"}],"abstract":"Natural Language Processing (NLP) - the branch of artificial intelligence (AI) concerned with the interaction between computers and human language - has advanced markedly in recent years with the introduction of sophisticated deep learning models. Improved performance in NLP tasks such as text and speech processing have fueled impressive demonstrations of these models' capabilities. Perhaps no demonstration has been more impactful to date than with the introduction of the publicly available online chatbot \"ChatGPT\" in November 2022 by OpenAI which is based on an NLP model known as a GPT (Generative Pretrained Transformer). Through a series of questions posed by the authors about Obstetrics and Gynecology to ChatGPT as prompts, we evaluated the model's ability to handle clinical related queries. Its answers demonstrate that in its current form, ChatGPT can be valuable for users who want preliminary information about virtually any topic in the field. As its educational role is being defined, we must recognize its limitations. While answers were generally eloquent, informed and lacked a significant degree of mistakes or misinformation, we also observed evidence of its weaknesses. A significant drawback is that the data on which the model has been trained are apparently not readily updated. The model assessed here seems to not reliably (if at all) source data after 2021. Users of ChatGPT who expect data to be more up to date need to be aware of this drawback. Inability to cite sources or truly understand what the user is asking suggests it has the capability to mislead. Responsible use of models like ChatGPT will be important in ensuring that they work to help but not harm users seeking information in Obstetrics and Gynecology.","source":"Semantic Scholar","year":2023,"language":"en","subjects":["Medicine"],"doi":"10.1016/j.ajog.2023.03.009","url":"https://www.semanticscholar.org/paper/364012e7f5d8e0e37d84f2b87bc3c116528a4993","is_open_access":true,"citations":186,"published_at":"","score":72.58},{"id":"ss_92fa2a86f2e78c7a0823bd0de69b4caab3c0b823","title":"Prognostic Performance of the 2018 International Federation of Gynecology and Obstetrics Cervical Cancer Staging Guidelines.","authors":[{"name":"J. Wright"},{"name":"K. Matsuo"},{"name":"Yongmei Huang"},{"name":"A. Tergas"},{"name":"J. Hou"},{"name":"F. Khoury‐Collado"},{"name":"C. S. St. Clair"},{"name":"C. Ananth"},{"name":"A. Neugut"},{"name":"D. Hershman"}],"abstract":"OBJECTIVE To examine the prognostic performance of the revised 2018 International Federation of Gynecology and Obstetrics (FIGO) cervical cancer staging schema. METHODS We used the National Cancer Database to identify women with cervical cancer diagnosed from 2004 to 2015. Using clinical and pathologic data, each patient's stage was classified using three staging schemas: American Joint Committee on Cancer 7th edition, FIGO 2009 and FIGO 2018. The FIGO 2018 revised staging classifies stage IB tumors into three substages based on tumor size (IB1-IB3) and classifies patients with positive lymph nodes (pathologically or clinically detected) as stage IIIC1 (positive pelvic nodes) or IIIC2 (positive para-aortic nodes). Five-year survival rates were estimated for each stage grouping. We sought to determine whether the 2018 FIGO staging system was able to offer improved 5-year survival rate differentiation compared with older staging schemas. RESULTS A total of 62,212 women were identified. The classification of stage IB tumors into three substages improved discriminatory ability. Five-year survival in the FIGO 2018 schema was 91.6% (95% CI 90.4-92.6%) for stage IB1 tumors, 83.3% (95% CI 81.8-84.8%) for stage IB2 neoplasms, and 76.1% (95% CI 74.3-77.8%) for IB3 lesions. In contrast, for women with stage III tumors, higher FIGO staging was not consistently associated with worse 5-year survival rates: stage IIIA (40.7%, 95 CI 37.1-44.3%), stage IIIB (41.4%; 95% CI 39.9-42.9%), stage IIIC1 (positive pelvic nodes) was 60.8% (95% CI 58.7-62.8%) and stage IIIC2 37.5% (95% CI 33.3-41.7%). CONCLUSION The FIGO 2018 staging schema provides improved discriminatory ability for women with stage IB tumors; however, classification of all women with positive lymph nodes into a single stage results in a very heterogeneous group of patients with highly variable survival rates.","source":"Semantic Scholar","year":2019,"language":"en","subjects":["Medicine"],"doi":"10.1097/AOG.0000000000003311","url":"https://www.semanticscholar.org/paper/92fa2a86f2e78c7a0823bd0de69b4caab3c0b823","pdf_url":"https://doi.org/10.1097/aog.0000000000003311","is_open_access":true,"citations":239,"published_at":"","score":70.17},{"id":"arxiv_2602.17513","title":"Bridging the Domain Divide: Supervised vs. Zero-Shot Clinical Section Segmentation from MIMIC-III to Obstetrics","authors":[{"name":"Baris Karacan"},{"name":"Barbara Di Eugenio"},{"name":"Patrick Thornton"}],"abstract":"Clinical free-text notes contain vital patient information. They are structured into labelled sections; recognizing these sections has been shown to support clinical decision-making and downstream NLP tasks. In this paper, we advance clinical section segmentation through three key contributions. First, we curate a new de-identified, section-labeled obstetrics notes dataset, to supplement the medical domains covered in public corpora such as MIMIC-III, on which most existing segmentation approaches are trained. Second, we systematically evaluate transformer-based supervised models for section segmentation on a curated subset of MIMIC-III (in-domain), and on the new obstetrics dataset (out-of-domain). Third, we conduct the first head-to-head comparison of supervised models for medical section segmentation with zero-shot large language models. Our results show that while supervised models perform strongly in-domain, their performance drops substantially out-of-domain. In contrast, zero-shot models demonstrate robust out-of-domain adaptability once hallucinated section headers are corrected. These findings underscore the importance of developing domain-specific clinical resources and highlight zero-shot segmentation as a promising direction for applying healthcare NLP beyond well-studied corpora, as long as hallucinations are appropriately managed.","source":"arXiv","year":2026,"language":"en","subjects":["cs.CL"],"url":"https://arxiv.org/abs/2602.17513","pdf_url":"https://arxiv.org/pdf/2602.17513","is_open_access":true,"published_at":"2026-02-19T16:25:07Z","score":70},{"id":"arxiv_2602.00726","title":"Augmenting Clinical Decision-Making with an Interactive and Interpretable AI Copilot: A Real-World User Study with Clinicians in Nephrology and Obstetrics","authors":[{"name":"Yinghao Zhu"},{"name":"Dehao Sui"},{"name":"Zixiang Wang"},{"name":"Xuning Hu"},{"name":"Lei Gu"},{"name":"Yifan Qi"},{"name":"Tianchen Wu"},{"name":"Ling Wang"},{"name":"Yuan Wei"},{"name":"Wen Tang"},{"name":"Zhihan Cui"},{"name":"Yasha Wang"},{"name":"Lequan Yu"},{"name":"Ewen M Harrison"},{"name":"Junyi Gao"},{"name":"Liantao Ma"}],"abstract":"Clinician skepticism toward opaque AI hinders adoption in high-stakes healthcare. We present AICare, an interactive and interpretable AI copilot for collaborative clinical decision-making. By analyzing longitudinal electronic health records, AICare grounds dynamic risk predictions in scrutable visualizations and LLM-driven diagnostic recommendations. Through a within-subjects counterbalanced study with 16 clinicians across nephrology and obstetrics, we comprehensively evaluated AICare using objective measures (task completion time and error rate), subjective assessments (NASA-TLX, SUS, and confidence ratings), and semi-structured interviews. Our findings indicate AICare's reduced cognitive workload. Beyond performance metrics, qualitative analysis reveals that trust is actively constructed through verification, with interaction strategies diverging by expertise: junior clinicians used the system as cognitive scaffolding to structure their analysis, while experts engaged in adversarial verification to challenge the AI's logic. This work offers design implications for creating AI systems that function as transparent partners, accommodating diverse reasoning styles to augment rather than replace clinical judgment.","source":"arXiv","year":2026,"language":"en","subjects":["cs.HC","cs.AI"],"url":"https://arxiv.org/abs/2602.00726","pdf_url":"https://arxiv.org/pdf/2602.00726","is_open_access":true,"published_at":"2026-01-31T13:41:32Z","score":70},{"id":"arxiv_2603.25886","title":"Automated Quality Assessment of Blind Sweep Obstetric Ultrasound for Improved Diagnosis","authors":[{"name":"Prasiddha Bhandari"},{"name":"Kanchan Poudel"},{"name":"Nishant Luitel"},{"name":"Bishram Acharya"},{"name":"Angelina Ghimire"},{"name":"Tyler Wellman"},{"name":"Kilian Koepsell"},{"name":"Pradeep Raj Regmi"},{"name":"Bishesh Khanal"}],"abstract":"Blind Sweep Obstetric Ultrasound (BSOU) enables scalable fetal imaging in low-resource settings by allowing minimally trained operators to acquire standardized sweep videos for automated Artificial Intelligence(AI) interpretation. However, the reliability of such AI systems depends critically on the quality of the acquired sweeps, and little is known about how deviations from the intended protocol affect downstream predictions. In this work, we present a systematic evaluation of BSOU quality and its impact on three key AI tasks: sweep-tag classification, fetal presentation classification, and placenta-location classification. We simulate plausible acquisition deviations, including reversed sweep direction, probe inversion, and incomplete sweeps, to quantify model robustness, and we develop automated quality-assessment models capable of detecting these perturbations. To approximate real-world deployment, we simulate a feedback loop in which flagged sweeps are re-acquired, showing that such correction improves downstream task performance. Our findings highlight the sensitivity of BSOU-based AI models to acquisition variability and demonstrate that automated quality assessment can play a central role in building reliable, scalable AI-assisted prenatal ultrasound workflows, particularly in low-resource environments.","source":"arXiv","year":2026,"language":"en","subjects":["cs.CV"],"url":"https://arxiv.org/abs/2603.25886","pdf_url":"https://arxiv.org/pdf/2603.25886","is_open_access":true,"published_at":"2026-03-26T20:20:21Z","score":70},{"id":"arxiv_2506.11356","title":"GynSurg: A Comprehensive Gynecology Laparoscopic Surgery Dataset","authors":[{"name":"Sahar Nasirihaghighi"},{"name":"Negin Ghamsarian"},{"name":"Leonie Peschek"},{"name":"Matteo Munari"},{"name":"Heinrich Husslein"},{"name":"Raphael Sznitman"},{"name":"Klaus Schoeffmann"}],"abstract":"Recent advances in deep learning have transformed computer-assisted intervention and surgical video analysis, driving improvements not only in surgical training, intraoperative decision support, and patient outcomes, but also in postoperative documentation and surgical discovery. Central to these developments is the availability of large, high-quality annotated datasets. In gynecologic laparoscopy, surgical scene understanding and action recognition are fundamental for building intelligent systems that assist surgeons during operations and provide deeper analysis after surgery. However, existing datasets are often limited by small scale, narrow task focus, or insufficiently detailed annotations, limiting their utility for comprehensive, end-to-end workflow analysis. To address these limitations, we introduce GynSurg, the largest and most diverse multi-task dataset for gynecologic laparoscopic surgery to date. GynSurg provides rich annotations across multiple tasks, supporting applications in action recognition, semantic segmentation, surgical documentation, and discovery of novel procedural insights. We demonstrate the dataset quality and versatility by benchmarking state-of-the-art models under a standardized training protocol. To accelerate progress in the field, we publicly release the GynSurg dataset and its annotations","source":"arXiv","year":2025,"language":"en","subjects":["cs.CV"],"url":"https://arxiv.org/abs/2506.11356","pdf_url":"https://arxiv.org/pdf/2506.11356","is_open_access":true,"published_at":"2025-06-12T23:10:47Z","score":69},{"id":"arxiv_2508.21398","title":"GLENDA: Gynecologic Laparoscopy Endometriosis Dataset","authors":[{"name":"Andreas Leibetseder"},{"name":"Sabrina Kletz"},{"name":"Klaus Schoeffmann"},{"name":"Simon Keckstein"},{"name":"Jörg Keckstein"}],"abstract":"Gynecologic laparoscopy as a type of minimally invasive surgery (MIS) is performed via a live feed of a patient's abdomen surveying the insertion and handling of various instruments for conducting treatment. Adopting this kind of surgical intervention not only facilitates a great variety of treatments, the possibility of recording said video streams is as well essential for numerous post-surgical activities, such as treatment planning, case documentation and education. Nonetheless, the process of manually analyzing surgical recordings, as it is carried out in current practice, usually proves tediously time-consuming. In order to improve upon this situation, more sophisticated computer vision as well as machine learning approaches are actively developed. Since most of such approaches heavily rely on sample data, which especially in the medical field is only sparsely available, with this work we publish the Gynecologic Laparoscopy ENdometriosis DAtaset (GLENDA) - an image dataset containing region-based annotations of a common medical condition named endometriosis, i.e. the dislocation of uterine-like tissue. The dataset is the first of its kind and it has been created in collaboration with leading medical experts in the field.","source":"arXiv","year":2025,"language":"en","subjects":["cs.CV","cs.MM"],"url":"https://arxiv.org/abs/2508.21398","pdf_url":"https://arxiv.org/pdf/2508.21398","is_open_access":true,"published_at":"2025-08-29T08:15:27Z","score":69},{"id":"arxiv_2502.18412","title":"Comparative Analysis of MDL-VAE vs. Standard VAE on 202 Years of Gynecological Data","authors":[{"name":"Paula Santos"}],"abstract":"This study presents a comparative evaluation of a Variational Autoencoder (VAE) enhanced with Minimum Description Length (MDL) regularization against a Standard Autoencoder for reconstructing high-dimensional gynecological data. The MDL-VAE exhibits significantly lower reconstruction errors (MSE, MAE, RMSE) and more structured latent representations, driven by effective KL divergence regularization. Statistical analyses confirm these performance improvements are significant. Furthermore, the MDL-VAE shows consistent training and validation losses and achieves efficient inference times, underscoring its robustness and practical viability. Our findings suggest that incorporating MDL principles into VAE architectures can substantially improve data reconstruction and generalization, making it a promising approach for advanced applications in healthcare data modeling and analysis.","source":"arXiv","year":2025,"language":"en","subjects":["cs.LG","cs.AI"],"doi":"10.5121/csit.2025.150403","url":"https://arxiv.org/abs/2502.18412","pdf_url":"https://arxiv.org/pdf/2502.18412","is_open_access":true,"published_at":"2025-02-25T18:05:46Z","score":69},{"id":"arxiv_2510.26563","title":"Fraction-variant VMAT planning for patients with complex gynecological and head-and-neck cancer","authors":[{"name":"Nathan Torelli"},{"name":"Madalyne Day"},{"name":"Jan Unkelbach"}],"abstract":"Background and Purpose: Increasing the number of arcs in volumetric modulated arc therapy (VMAT) allows for better intensity modulation and may improve plan quality. However, this leads to longer delivery times, which may cause patient discomfort and increase intra-fractional motion. In this study, it was investigated whether the delivery of different VMAT plans in different fractions may improve the dosimetric quality and delivery efficiency for the treatment of patients with complex tumor geometries.   Materials and Methods: A direct aperture optimization algorithm was developed which allows for the simultaneous optimization of different VMAT plans to be delivered in different fractions, based on their cumulative physical dose. Each VMAT plan is constrained to deliver a uniform dose within the target volume, such that the entire treatment does not alter the fractionation scheme and is robust against inter-fractional setup errors. This approach was evaluated in-silico for ten patients with gynecological and head-and-neck cancer.   Results: For all patients, fraction-variant treatments achieved better target coverage and reduced the dose to critical organs-at-risk compared to fraction-invariant treatments that deliver the same plan in every fraction, where the dosimetric benefit was shown to increase with the number of different plans to be delivered. In addition, 1-arc and 2-arc fraction-variant treatments could approximate the dosimetric quality of 3-arc fraction-invariant treatments, while reducing the delivery time from 180 s to 60 s and 120 s, respectively.   Conclusions: Fraction-variant VMAT treatments may achieve excellent dosimetric quality for patients with complex tumor geometries, while keeping the delivery time per fraction viable.","source":"arXiv","year":2025,"language":"en","subjects":["physics.med-ph"],"url":"https://arxiv.org/abs/2510.26563","pdf_url":"https://arxiv.org/pdf/2510.26563","is_open_access":true,"published_at":"2025-10-30T14:53:19Z","score":69},{"id":"arxiv_2506.01073","title":"A Large Convolutional Neural Network for Clinical Target and Multi-organ Segmentation in Gynecologic Brachytherapy with Multi-stage Learning","authors":[{"name":"Mingzhe Hu"},{"name":"Yuan Gao"},{"name":"Yuheng Li"},{"name":"Ricahrd LJ Qiu"},{"name":"Chih-Wei Chang"},{"name":"Keyur D. Shah"},{"name":"Priyanka Kapoor"},{"name":"Beth Bradshaw"},{"name":"Yuan Shao"},{"name":"Justin Roper"},{"name":"Jill Remick"},{"name":"Zhen Tian"},{"name":"Xiaofeng Yang"}],"abstract":"Purpose: Accurate segmentation of clinical target volumes (CTV) and organs-at-risk is crucial for optimizing gynecologic brachytherapy (GYN-BT) treatment planning. However, anatomical variability, low soft-tissue contrast in CT imaging, and limited annotated datasets pose significant challenges. This study presents GynBTNet, a novel multi-stage learning framework designed to enhance segmentation performance through self-supervised pretraining and hierarchical fine-tuning strategies. Methods: GynBTNet employs a three-stage training strategy: (1) self-supervised pretraining on large-scale CT datasets using sparse submanifold convolution to capture robust anatomical representations, (2) supervised fine-tuning on a comprehensive multi-organ segmentation dataset to refine feature extraction, and (3) task-specific fine-tuning on a dedicated GYN-BT dataset to optimize segmentation performance for clinical applications. The model was evaluated against state-of-the-art methods using the Dice Similarity Coefficient (DSC), 95th percentile Hausdorff Distance (HD95), and Average Surface Distance (ASD). Results: Our GynBTNet achieved superior segmentation performance, significantly outperforming nnU-Net and Swin-UNETR. Notably, it yielded a DSC of 0.837 +/- 0.068 for CTV, 0.940 +/- 0.052 for the bladder, 0.842 +/- 0.070 for the rectum, and 0.871 +/- 0.047 for the uterus, with reduced HD95 and ASD compared to baseline models. Self-supervised pretraining led to consistent performance improvements, particularly for structures with complex boundaries. However, segmentation of the sigmoid colon remained challenging, likely due to anatomical ambiguities and inter-patient variability. Statistical significance analysis confirmed that GynBTNet's improvements were significant compared to baseline models.","source":"arXiv","year":2025,"language":"en","subjects":["cs.CV"],"url":"https://arxiv.org/abs/2506.01073","pdf_url":"https://arxiv.org/pdf/2506.01073","is_open_access":true,"published_at":"2025-06-01T16:21:48Z","score":69},{"id":"doaj_10.1186/s12905-025-03587-5","title":"The impact of progestogens on RAAS – a systematic review","authors":[{"name":"Adrian Singer"},{"name":"Katharina Tropschuh"},{"name":"Marc von Gernler"},{"name":"Claire Decrinis"},{"name":"Petra Stute"}],"abstract":"Abstract Background Progestogens, synthetic analogues of progesterone, are widely used in clinical practice for contraception, hormone replacement therapy, and the management of gynecological disorders. Understanding the specific impacts of different progestogens on the renin-angiotensin-aldosterone system (RAAS) is crucial due to their potential effects on cardiovascular and renal outcomes. Objective This systematic review aims to synthesize existing research on the effects of various progestogens on the RAAS and associated clinical outcomes. Methods We conducted a comprehensive search of databases up to the search date, including randomized controlled trials (RCTs), cohort studies, case-control studies, cross-sectional studies, and qualitative studies. The NIH Study Quality Assessment Tool for Controlled Intervention Studies was used to evaluate the quality of the included studies. Data extraction and quality assessment were performed independently by two reviewers, with discrepancies resolved through discussion. Results Forty-two studies on drospirenone (DRSP) were the most extensively investigated, showing either decreased or unchanged blood pressure (BP), mostly unchanged serum sodium, and an increased risk of hyperkalemia only in patients with mild renal impairment. Sixteen studies on norethindrone (NET/NETA) presented conflicting results on BP and a higher risk of hyperkalemia. Other progestogens, such as levonorgestrel (LNG) and medroxyprogesterone acetate (MPA), showed varied effects on RAAS parameters. Notably, changes in plasma renin activity (PRA), serum aldosterone, and angiotensin II levels were inconsistent across different progestogens and study designs. Conclusion The effects of progestogens on the RAAS are complex and varied, influenced by the type of progestogen, dosage, and combination with estrogen. While some progestogens like DRSP may offer benefits in BP management with minimal electrolyte disturbances, others like NET/NETA might require more careful monitoring due to their associated risks. These findings highlight the importance of personalized medicine approaches in the use of progestogens, tailored to individual patient characteristics and specific hormonal profiles. Further research with standardized methodologies is needed to clarify these effects and guide clinical practice. Trial registration This review was prospectively registered with PROSPERO.","source":"DOAJ","year":2025,"language":"","subjects":["Gynecology and obstetrics","Public aspects of medicine"],"doi":"10.1186/s12905-025-03587-5","url":"https://doi.org/10.1186/s12905-025-03587-5","is_open_access":true,"published_at":"","score":69},{"id":"ss_5eb684ad8d7c095f7754ec70f8cd40711362edab","title":"European Journal of Obstetrics \u0026 Gynecology and Reproductive Biology","authors":[{"name":"Lan N. Vuong"}],"abstract":"","source":"Semantic Scholar","year":2021,"language":"en","subjects":null,"url":"https://www.semanticscholar.org/paper/5eb684ad8d7c095f7754ec70f8cd40711362edab","is_open_access":true,"citations":129,"published_at":"","score":68.87},{"id":"ss_afe9afa1d8a5e1b415562a3d1eff3f2add40d518","title":"Assisted reproductive technology in Japan: A summary report for 2020 by the ethics Committee of the Japan Society of obstetrics and gynecology","authors":[{"name":"Y. Katagiri"},{"name":"S. Jwa"},{"name":"Akira Kuwahara"},{"name":"T. Iwasa"},{"name":"M. Ono"},{"name":"K. Kato"},{"name":"H. Kishi"},{"name":"Y. Kuwabara"},{"name":"M. Harada"},{"name":"T. Hamatani"},{"name":"Y. Osuga"}],"abstract":"Since 1986, the Japan Society of Obstetrics and Gynecology assisted reproductive technology (ART) registry system has collected data on national ART use and outcomes trends in Japan. Herein, we describe the characteristics and outcomes of ART cycles registered during 2020 and compare the results with those from 2019.","source":"Semantic Scholar","year":2023,"language":"en","subjects":["Medicine"],"doi":"10.1002/rmb2.12494","url":"https://www.semanticscholar.org/paper/afe9afa1d8a5e1b415562a3d1eff3f2add40d518","pdf_url":"https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/rmb2.12494","is_open_access":true,"citations":41,"published_at":"","score":68.22999999999999},{"id":"ss_db5ee3b78059fdb52e9bfe3a022f04bd162c822c","title":"Evolving the Era of 5D Ultrasound? A Systematic Literature Review on the Applications for Artificial Intelligence Ultrasound Imaging in Obstetrics and Gynecology","authors":[{"name":"Elena Jost"},{"name":"P. Kosian"},{"name":"J. Jiménez Cruz"},{"name":"Shadi Albarqouni"},{"name":"U. Gembruch"},{"name":"B. Strizek"},{"name":"F. Recker"}],"abstract":"Artificial intelligence (AI) has gained prominence in medical imaging, particularly in obstetrics and gynecology (OB/GYN), where ultrasound (US) is the preferred method. It is considered cost effective and easily accessible but is time consuming and hindered by the need for specialized training. To overcome these limitations, AI models have been proposed for automated plane acquisition, anatomical measurements, and pathology detection. This study aims to overview recent literature on AI applications in OB/GYN US imaging, highlighting their benefits and limitations. For the methodology, a systematic literature search was performed in the PubMed and Cochrane Library databases. Matching abstracts were screened based on the PICOS (Participants, Intervention or Exposure, Comparison, Outcome, Study type) scheme. Articles with full text copies were distributed to the sections of OB/GYN and their research topics. As a result, this review includes 189 articles published from 1994 to 2023. Among these, 148 focus on obstetrics and 41 on gynecology. AI-assisted US applications span fetal biometry, echocardiography, or neurosonography, as well as the identification of adnexal and breast masses, and assessment of the endometrium and pelvic floor. To conclude, the applications for AI-assisted US in OB/GYN are abundant, especially in the subspecialty of obstetrics. However, while most studies focus on common application fields such as fetal biometry, this review outlines emerging and still experimental fields to promote further research.","source":"Semantic Scholar","year":2023,"language":"en","subjects":["Medicine"],"doi":"10.3390/jcm12216833","url":"https://www.semanticscholar.org/paper/db5ee3b78059fdb52e9bfe3a022f04bd162c822c","pdf_url":"https://www.mdpi.com/2077-0383/12/21/6833/pdf?version=1698576129","is_open_access":true,"citations":36,"published_at":"","score":68.08},{"id":"ss_7b7cd05e91e141321a4bc54d755b38289077418e","title":"Diagnostic and Management Performance of ChatGPT in Obstetrics and Gynecology","authors":[{"name":"L. Allahqoli"},{"name":"M. Ghiasvand"},{"name":"A. Mazidimoradi"},{"name":"H. Salehiniya"},{"name":"I. Alkatout"}],"abstract":"Objectives: The use of artificial intelligence (AI) in clinical patient management and medical education has been advancing over time. ChatGPT was developed and trained recently, using a large quantity of textual data from the internet. Medical science is expected to be transformed by its use. The present study was conducted to evaluate the diagnostic and management performance of the ChatGPT AI model in obstetrics and gynecology. Design: A cross-sectional study was conducted. Participants/Materials, Setting, Methods: This study was conducted in Iran in March 2023. Medical histories and examination results of 30 cases were determined in six areas of obstetrics and gynecology. The cases were presented to a gynecologist and ChatGPT for diagnosis and management. Answers from the gynecologist and ChatGPT were compared, and the diagnostic and management performance of ChatGPT were determined. Results: Ninety percent (27 of 30) of the cases in obstetrics and gynecology were correctly handled by ChatGPT. Its responses were eloquent, informed, and free of a significant number of errors or misinformation. Even when the answers provided by ChatGPT were incorrect, the responses contained a logical explanation about the case as well as information provided in the question stem. Limitations: The data used in this study were taken from the electronic book and may reflect bias in the diagnosis of ChatGPT. Conclusions: This is the first evaluation of ChatGPT’s performance in diagnosis and management in the field of obstetrics and gynecology. It appears that ChatGPT has potential applications in the practice of medicine and is (currently) free and simple to use. However, several ethical considerations and limitations such as bias, validity, copyright infringement, and plagiarism need to be addressed in future studies.","source":"Semantic Scholar","year":2023,"language":"en","subjects":["Medicine"],"doi":"10.1159/000533177","url":"https://www.semanticscholar.org/paper/7b7cd05e91e141321a4bc54d755b38289077418e","is_open_access":true,"citations":35,"published_at":"","score":68.05},{"id":"arxiv_2405.00234","title":"Conceiving Naturally After IVF: the effect of assisted reproduction on obstetric interventions and child health at birth","authors":[{"name":"Fabio I. Martinenghi"},{"name":"Xian Zhang"},{"name":"Luk Rombauts"},{"name":"Georgina M. Chambers"}],"abstract":"A growing share of the world's population is being born via assisted reproductive technology (ART), including in-vitro fertilisation (IVF). However, two concerns persist. First, ART pregnancies correlate with predictors of poor outcomes at birth--and it is unclear whether this relationship is causal. Second, the emotional and financial costs associated with ART-use might exacerbate defensive medical behaviour, where physicians intervene more than necessary to reduce the risk of adverse medical outcomes and litigation. We address the challenge of identifying the pure effect of ART-use on both maternal and infant outcomes at birth by leveraging exogenous variation in the success of ART cycles. We compare the obstetric outcomes for ART-conceived births with those of spontaneously-conceived births after a failed ART treatment. Moreover, we flexibly adjust for key confounders using double machine learning. We do this using clinical registry ART data and administrative maternal and infant data from New South Wales (NSW) between 2009-2017. We find that ART slightly decreases the risk of obstetric interventions, lowering the risk of a caesarean section and increasing the rate of spontaneous labour (+3.5 p.p.). Moreover, we find that ART has a statistically and clinically insignificant effect on infant health outcomes.   Keywords: Fertility, Assisted reproduction, IVF, Caesarean Section, Obstetric, Infertility. JEL classification: I10, I12, I19.","source":"arXiv","year":2024,"language":"en","subjects":["econ.GN"],"url":"https://arxiv.org/abs/2405.00234","pdf_url":"https://arxiv.org/pdf/2405.00234","is_open_access":true,"published_at":"2024-04-30T23:00:30Z","score":68},{"id":"arxiv_2409.00544","title":"Large Language Models-Enabled Digital Twins for Precision Medicine in Rare Gynecological Tumors","authors":[{"name":"Jacqueline Lammert"},{"name":"Nicole Pfarr"},{"name":"Leonid Kuligin"},{"name":"Sonja Mathes"},{"name":"Tobias Dreyer"},{"name":"Luise Modersohn"},{"name":"Patrick Metzger"},{"name":"Dyke Ferber"},{"name":"Jakob Nikolas Kather"},{"name":"Daniel Truhn"},{"name":"Lisa Christine Adams"},{"name":"Keno Kyrill Bressem"},{"name":"Sebastian Lange"},{"name":"Kristina Schwamborn"},{"name":"Martin Boeker"},{"name":"Marion Kiechle"},{"name":"Ulrich A. Schatz"},{"name":"Holger Bronger"},{"name":"Maximilian Tschochohei"}],"abstract":"Rare gynecological tumors (RGTs) present major clinical challenges due to their low incidence and heterogeneity. The lack of clear guidelines leads to suboptimal management and poor prognosis. Molecular tumor boards accelerate access to effective therapies by tailoring treatment based on biomarkers, beyond cancer type. Unstructured data that requires manual curation hinders efficient use of biomarker profiling for therapy matching. This study explores the use of large language models (LLMs) to construct digital twins for precision medicine in RGTs.   Our proof-of-concept digital twin system integrates clinical and biomarker data from institutional and published cases (n=21) and literature-derived data (n=655 publications with n=404,265 patients) to create tailored treatment plans for metastatic uterine carcinosarcoma, identifying options potentially missed by traditional, single-source analysis. LLM-enabled digital twins efficiently model individual patient trajectories. Shifting to a biology-based rather than organ-based tumor definition enables personalized care that could advance RGT management and thus enhance patient outcomes.","source":"arXiv","year":2024,"language":"en","subjects":["cs.CL","cs.AI","q-bio.QM","stat.ML"],"doi":"10.1038/s41746-025-01810-z","url":"https://arxiv.org/abs/2409.00544","pdf_url":"https://arxiv.org/pdf/2409.00544","is_open_access":true,"published_at":"2024-08-31T21:14:09Z","score":68},{"id":"arxiv_2402.17960","title":"Rapid hyperspectral photothermal mid-infrared spectroscopic imaging from sparse data for gynecologic cancer tissue subtyping","authors":[{"name":"Reza Reihanisaransari"},{"name":"Chalapathi Charan Gajjela"},{"name":"Xinyu Wu"},{"name":"Ragib Ishrak"},{"name":"Sara Corvigno"},{"name":"Yanping Zhong"},{"name":"Jinsong Liu"},{"name":"Anil K. Sood"},{"name":"David Mayerich"},{"name":"Sebastian Berisha"},{"name":"Rohith Reddy"}],"abstract":"Ovarian cancer detection has traditionally relied on a multi-step process that includes biopsy, tissue staining, and morphological analysis by experienced pathologists. While widely practiced, this conventional approach suffers from several drawbacks: it is qualitative, time-intensive, and heavily dependent on the quality of staining. Mid-infrared (MIR) hyperspectral photothermal imaging is a label-free, biochemically quantitative technology that, when combined with machine learning algorithms, can eliminate the need for staining and provide quantitative results comparable to traditional histology. However, this technology is slow. This work presents a novel approach to MIR photothermal imaging that enhances its speed by an order of magnitude. Our method significantly accelerates data collection by capturing a combination of high-resolution and interleaved, lower-resolution infrared band images and applying computational techniques for data interpolation. We effectively minimize data collection requirements by leveraging sparse data acquisition and employing curvelet-based reconstruction algorithms. This method enables the reconstruction of high-quality, high-resolution images from undersampled datasets and achieving a 10X improvement in data acquisition time. We assessed the performance of our sparse imaging methodology using a variety of quantitative metrics, including mean squared error (MSE), structural similarity index (SSIM), and tissue subtype classification accuracies, employing both random forest and convolutional neural network (CNN) models, accompanied by ROC curves. Our statistically robust analysis, based on data from 100 ovarian cancer patient samples and over 65 million data points, demonstrates the method's capability to produce superior image quality and accurately distinguish between different gynecological tissue types with segmentation accuracy exceeding 95%.","source":"arXiv","year":2024,"language":"en","subjects":["cs.CV","q-bio.BM","q-bio.QM","q-bio.TO"],"url":"https://arxiv.org/abs/2402.17960","pdf_url":"https://arxiv.org/pdf/2402.17960","is_open_access":true,"published_at":"2024-02-28T00:57:35Z","score":68}],"total":620937,"page":1,"page_size":20,"sources":["arXiv","DOAJ","Semantic Scholar","CrossRef"],"query":"Gynecology and obstetrics"}